Statistics and Methodology - PSYC8010

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2021 to 2022
Combined Autumn and Spring Terms 7 40 (20) Roger Giner-Sorolla checkmark-circle


This module provides a postgraduate-level orientation to both basic and advanced contemporary statistical and methodological issues in psychology. The methodological issues considered include qualitative research methodologies; experimental, quasi-experimental, and correlational research designs in the laboratory and field; and the fundamental issues in psychological measurement including reliability and validity. The statistical techniques taught include univariate and multivariate descriptive and inferential statistics; basic and advanced topics in ANOVA and ANCOVA; linear and logistic multiple regression; some scaling methods; classical test theory, factor analysis; fundamentals of structural equation modelling (path analysis, confirmatory factor analysis, multiple-group analysis), and some item response theory.


Contact hours

Total contact hours: 110
Private study hours: 290
Total study hours: 400


Compulsory to:

Developmental Psychology MSc-T
Forensic Psychology, MSc-T
Political Psychology, MSc-T
Group Processes, MSc-T
Cognitive Psychology/Neuropsychology, MSc-T
Social and Applied Psychology MSc-T
Research Methods in Psychology, MSc-T
Evolution and Human Behaviour, MSc-T
Political Psychology, MSc-T

Method of assessment

Two In Class Tests, each formed of a separate 45 minute theory paper and a separate 2 hour computing paper.

Reassessment methods: Like for Like.

Indicative reading

Reading list (Indicative list, current at time of publication. Reading lists will be published annually)

Howell, D. C. (2006). Statistical methods for psychology (International Ed.). Belmont, CA: Duxbury Press. (Recommended for those in need of review of statistics; covers similar topics to the Autumn term, but many Autumn topics are not covered in any basic-level text.)

Dancey, C. P., & Ready, J. (2007). Statistics without maths for psychology (4th ed.). London: Pearson. (Exactly what it says; good review of basic concepts and SPSS for the equation-phobic)

Field, A. (2012 or 2013). Discovering statistics using SPSS (3rd edition of higher) London: Sage. (Another introduction focusing on using SPSS)

McDonald, R.P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum. (Relevant to the spring term; a great source of knowledge on psychometrics; particularly recommended for those who prefer concise algebraic treatment rather than a lot of text).

Kline, R. B. (2010). Principles and practice of Structural Equation Modeling (3rd ed.). New York: Guilford Press. (Relevant to the spring term; goes into more depth than the lectures)

Byrne, B. M. (2010). Structural equation modeling with Amos: Basic concepts, applications, and programming (2nd ed.). New York, NY: Taylor and Francis Group. (Relevant to the spring term; a step-by-step guide to fitting measurement models in AMOS; good companion when trying to model your own data).

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the module students will be able to:

8.1. Demonstrate a systemic understanding of the complex concepts and logic of statistical reasoning, using appropriate descriptive and inferential methods;

8.2. Comprehensively understand the fundamentals of scaling and methods used for measuring psychological variables;

8.3. Demonstrate a systemic understanding of the concepts of statistical model and model testing;

8.4. Use software SPSS to manage data, conduct descriptive analyses and test hypotheses; use software AMOS to specify and test structural equation models;

8.5. Interpret results of statistical analyses and outputs of statistical software; make inferences from the results in applied settings;

8.6. Systematically evaluate the appropriateness of statistical analysis methods to research design and data;

8.7. Effectively communicate results of statistical analyses orally and in writing.

8.8. Demonstrate a systemic understanding of how to apply qualitative, correlational and experimental research methods

The intended generic learning outcomes. On successfully completing the module students will be able to:

9.1 Demonstrate an understanding of complex theoretical positions and controversies related to methodology;

9.2 Demonstrate an appreciation of the diverse applications of statistics and its relevance to students' fields of study and social sciences more broadly.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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